Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

Numerous deep learning architectures have been developed to accommodate the diversity
of time-series datasets across different domains. In this article, we survey common encoder …

The recent boom of linear forecasting models questions the ongoing passion for
architectural modifications of Transformer-based forecasters. These forecasters leverage …

Recently many deep models have been proposed for multivariate time series (MTS)
forecasting. In particular, Transformer-based models have shown great potential because …

Transformers have shown great power in time series forecasting due to their global-range
modeling ability. However, their performance can degenerate terribly on non-stationary real …

Real-world time-series datasets are often multivariate with complex dynamics. To capture
this complexity, high capacity architectures like recurrent-or attention-based sequential deep …

Long-term time series forecasting is challenging since prediction accuracy tends to
decrease dramatically with the increasing horizon. Although Transformer-based methods …

Extending the forecasting time is a critical demand for real applications, such as extreme
weather early warning and long-term energy consumption planning. This paper studies the …

Recent studies have shown that deep learning models such as RNNs and Transformers
have brought significant performance gains for long-term forecasting of time series because …

One unique property of time series is that the temporal relations are largely preserved after
downsampling into two sub-sequences. By taking advantage of this property, we propose a …